New insurance framework prevents AI agents from gaming their contracts

A new academic paper from Hao-Hsuan Chen tackles a critical problem in autonomous AI: ensuring insurance contracts for agent behavior cannot be gamed by clever operators. Building on an earlier foundation (Paper A) that priced each side-effect-bearing action against a safe default and gated execution via a reserve budget, this work makes the operator strategic. It maps out a five-attack space that could be exploited to reduce insurance costs unfairly, and proves which attacks are already blocked by Paper A's minimal-authority and no-splitting clauses.

The remaining three attack surfaces require novel contract clauses. First, common-control aggregation prevents operators from routing actions across boundaries to lower toll costs below the total exposure charge. Second, the interface-compliance theorem treats invalid JSON or other interface failures as contract-relevant events, not safety wins—escalation fees reverse the incentive to use unreliable models. Third, a model-identity menu with a componentwise-minimum penalty schedule makes truthful reporting of the deployed AI model the dominant strategy. Combined with Paper A's runtime guarantees, these clauses achieve joint incentive compatibility across all five attacks. A two-parameter premium family then satisfies operator individual rationality and weak budget balance at the truthful equilibrium, creating a practical incentive-compatibility layer for autonomous-agent side-effect control.